Almost Sure Adaptive Synchronization for Neutral-Type Neural Networks with Markovian Switching

被引:0
|
作者
Yang Xueqing [1 ]
Zhou Wuneng [1 ]
Yang Jun [1 ]
Dai Anding [1 ]
机构
[1] Donghua Univ, Sch Informat Sci & Technol, Shanghai 200051, Peoples R China
关键词
Neutral-Type Neural Network; Adaptive Synchronization; Stochastic Perturbation; Markovian Switching; Linear Matrix Inequality; DIFFERENTIAL-DELAY EQUATIONS; EXPONENTIAL SYNCHRONIZATION; LAG SYNCHRONIZATION; DISTRIBUTED DELAYS; ROBUST STABILITY; TIME-DELAYS; PERTURBATION; DISCRETE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The problem of almost sure (a. s.) adaptive synchronization for neutral-type neural networks with Markovian switching is researched in this paper. A new criterion of a. s. asymptotic stability for a general neutral-type stochastic differential equation which extends the existed results is given firstly. Next, based upon this stability criterion, by making use of Lyapunov functional method and designing a adaptive controller, a condition of a. s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching is obtained. The synchronization condition is expressed as linear matrix inequality which can be easily solved by Matlab. A numerical example to illustrate the effectiveness of the method and result is introduced finally.
引用
收藏
页码:4963 / 4969
页数:7
相关论文
共 50 条
  • [1] Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching
    Zhou, Wuneng
    Yang, Xueqing
    Yang, Jun
    Dai, Anding
    Liu, Huashan
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2014, 2014
  • [2] Adaptive almost sure asymptotically stability for neutral-type neural networks with stochastic perturbation and Markovian switching
    Zhou, Liuwei
    Wang, Zhijie
    Hu, Xiantao
    Chu, Bo
    Zhou, Wuneng
    [J]. NEUROCOMPUTING, 2015, 156 : 151 - 156
  • [3] Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching Parameters
    Zhou, Wuneng
    Zhu, Qingyu
    Shi, Peng
    Su, Hongye
    Fang, Jian'an
    Zhou, Liuwei
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2014, 44 (12) : 2848 - 2860
  • [4] Almost sure adaptive asymptotically synchronization for neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic perturbation
    Zhou, Jun
    Ding, Xiangwu
    Zhou, Liuwei
    Zhou, Wuneng
    Yang, Jun
    Tong, Dongbing
    [J]. NEUROCOMPUTING, 2016, 214 : 44 - 52
  • [5] Adaptive exponential synchronization in pth moment of neutral-type neural networks with time delays and Markovian switching
    Wuneng Zhou
    Yan Gao
    Dongbing Tong
    Chuan Ji
    Jian’an Fang
    [J]. International Journal of Control, Automation and Systems, 2013, 11 : 845 - 851
  • [6] Adaptive Exponential Synchronization in pth Moment of Neutral-Type Neural Networks with Time Delays and Markovian Switching
    Zhou, Wuneng
    Gao, Yan
    Tong, Dongbing
    Ji, Chuan
    Fang, Jian'an
    [J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2013, 11 (04) : 845 - 851
  • [7] Exponential Synchronization for Markovian Stochastic Coupled Neural Networks of Neutral-Type via Adaptive Feedback Control
    Chen, Huabin
    Shi, Peng
    Lim, Cheng-Chew
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (07) : 1618 - 1632
  • [8] Adaptive almost surely asymptotically synchronization for stochastic delayed neural networks with Markovian switching
    Ding, Xiangwu
    Gao, Yan
    Zhou, Wuneng
    Tong, Dongbing
    Su, Hongye
    [J]. ADVANCES IN DIFFERENCE EQUATIONS, 2013,
  • [9] Adaptive almost surely asymptotically synchronization for stochastic delayed neural networks with Markovian switching
    Xiangwu Ding
    Yan Gao
    Wuneng Zhou
    Dongbing Tong
    Hongye Su
    [J]. Advances in Difference Equations, 2013
  • [10] Almost Sure Exponential Stability of Recurrent Neural Networks With Markovian Switching
    Shen, Yi
    Wang, Jun
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2009, 20 (05): : 840 - 855